We consider shared autonomy systems where multiple operators (AI and human), can interact with the environment, e.g. by controlling a robot. The decision problem for the shared autonomy system is to select which operator takes control at each timestep, such that a reward specifying the intended system behaviour is maximised. The performance of the human operator is influenced by unobserved factors, such as fatigue or skill level. Therefore, the system must reason over stochastic models of operator performance. We present a framework for stochastic operators in shared autonomy systems (SO-SAS), where we represent operators using rich, partially observable models. We formalise SO-SAS as a mixed-observability Markov decision process, where environment states are fully observable and internal operator states are hidden. We test SO-SAS on a simulated domain and a computer game, empirically showing it results in better performance compared to traditional formulations of shared autonomy systems.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Shared autonomy systems with stochastic operator models


    Beteiligte:
    Costen, C (Autor:in) / Rigter, M (Autor:in) / Lacerda, B (Autor:in) / Hawes, N (Autor:in)

    Erscheinungsdatum :

    06.05.2022


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629



    Shared Autonomy for Remote Collaboration

    CAMILLI RICHARD / PHUNG AMY / BILLINGS GIDEON et al. | Europäisches Patentamt | 2024

    Freier Zugriff


    Reinforcement Learning for Shared Autonomy Drone Landings

    Backman, Kal / Kulić, Dana / Chung, Hoam | ArXiv | 2022

    Freier Zugriff

    Optimization for Shared-Autonomy in Automotive Swarm Environment

    Sengstacken, Aaron / DeLaurentis, Daniel / Bai, Sue | SAE Technical Papers | 2009


    Optimization of shared-autonomy in automotive swarm environment

    Bai,S. / Sengstacken,A. / DeLaurentis,D. et al. | Kraftfahrwesen | 2009